Artificial Intelligence Fault Diagnosis Techniques Applied to a Wind Turbine Simulator

The fault diagnosis and prognosis of wind turbine systems represent a challenging issue, thus justifying the research topics developed in this work with application to safety critical systems. Therefore, this paper addresses these research issues and demostrates viable techniques of fault diagnosis and condition monitoring. To this aim, the design of the so--called fault detector relise on its estimate, which involves data--driven methods, as they result effective methods for managing partial information of the system dynamics, together with errors, model--reality mismatch and disturbance effects. In particular, the considered data--driven strategies use fuzzy systems and neural networks, which are employed to establish nonlinear dynamic links between measurements and faults. The selected prototypes are based on nonlinear autoregressive with exogenous input descriptions, since they are able to approximate nonlinear dynamic functions with arbitrary degree of accuracy. The capabilities of the designed fault diagnosis schemes are verified via a high--fidelity simulator, which describes the normal and the faulty behaviour of a wind turbine plant. Finally, the robustness and the reliability features of the proposed methods are validated in the presence of uncertainty and disturbance implemented in the wind turbine simulator.